We evaluate the HGCN+ (3500 hours training data) on the DNS-2020 and the DNS-2022 test sets. Because HGCN+ processes the HB and WB spectra separately, and the WB spectrum contains more speech information, the model has good performance on the WB test set can bring gains to the processing of FB signal, as shown in Table REF . In DNS-2022, ITU-T P.835 framework [1]} and Word Accuracy (WAcc) are used to evaluate the speech quality, and HGCN+ outperforms the baseline [2]} with less parameters in all metrics, as shown in Table REF . By audiometric analysis, we also found that non-speech sound, such as breath, sob, etc. is usually regarded as noise and filtered out, which results in incomplete human voice but better speech and the BAK MOS which is relatively larger than SIG MOS. In addition, our method makes a confusing pitch choice in the case of multi-speaker and degrades the performance also. The RTF of HGCN+ in processing the FB signal is 0.16 and it consumes 5.22 ms per frame. The frame size and overlap are 32 ms and 25%. Since the model is causal without looking forward, the latency is \(32\text{+}8\text{=}40\)  ms, which satisfies the requirements of the challenge.
